United States

Automatic building extraction from satellite RGB images, is a low-cost alternative to perform important urban planning tasks. Yet, itis a challenging one, especially when natural and non-city block objects interfere in the semantic segmentation of algorithms that extract their key features. In this work we approach the automatic building extraction using a Convolution Neural Network based on the U-Net architecture. In contrast to existing approaches, it successfully encodes important features and decodes the buildings’ localization by requiring both reduced computational time and dataset size. We evaluate the U-Net’s performance using RGB images selected from the SpaceNet 1 dataset and the experimental results show an accuracy in building localization of 92.3%. Finally,favorable comparison with existing CNN approaches to hyper-spectral images targeting the SpaceNet 1 dataset, demonstrated its effectiveness.

PETRA 2021

Building Extraction from RGB Satellite Images using Deep Learning: A U-Net Approach

automatic building extraction

remote sensing

deep learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

workshop paper

**About**
 The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

 PETRA (which means “stone” in Greek) addresses important human living needs. From a technological perspective, PETRA motivates and inspires new scientific thrusts, innovations and discoveries to improve living environments, a particularly important issue, in an increasingly crowded and complex world where the need for inclusiveness and connectivity with the rest of the world is key. For the elderly in particular, PETRA addresses the fact that, as people grow older; they will increasingly rely on technology to be able to prolong staying in their homes and enjoy an independent life.

 The PETRA 2021 proceedings will be published in the ACM Digital Library as part of the ACM ICPS program. The ISBN number assigned to PETRA '21 is 978-1-4503-8792-7

The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

Currently, spatial geographic data can be collected for many applications
that involve data on the planet earth. These collected
data typically have coordinates (x, y), or longitude and latitude in
map space, and thus can be located and displayed on maps. Data
alone represents facts and has no meaning on its own but becomes
meaningful when it is associated with application knowledge, such
as elections, crimes, disease, etc. For example, there is no meaning
behind those numbers (1, 23, 125, 355, . . .), yet they are data that
can get meaning when correlated with the total number of cases of
COVID-19 in Texas per day starting on a certain date. Many devices
can provide sequences of object location data over time (GPS in
vehicles or mobile devices, etc.). However, no device can visualize or
display them on its own without a visualization App. Both numeric
and location data are raw data that need to be pre-processed and
cleaned to become meaningful. Currently, collected data is a very
valuable source of information which, after collection, can be processed,
stored, analyzed, and visualized. In this paper, the available
techniques of spatial data visualization will be overviewed. Moreover,
a case study of COVID-19 spatio-temporal data visualization,
using one of the techniques will be demonstrated. The COVID-19
data will be spatially visualized when data on a specific date is
queried for analysis. On the other hand, spatio-temporal visualization
will be displayed when a time series of COVID-19 data is
queried for analysis.

Map Visualization using Spatial and Spatio-Temporal Data: Application to COVID-19 Data

In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.

CO2 Meter: A do-it-yourself carbon dioxide measuring device for the classroom

Over the past few years, the ubiquitous usage of internet to broadcast information worldwide, has proved to be one of the best methods in making people aware about their surroundings. This has also led towards storage of vast amount of data in user interactive websites. Several news channels apart from live streaming, are using internet in such ways to convey their information. And these methods have not only benefited people to acquire regular updates but have also impacted their lives in many ways during the world awakening pandemic like COVID-19. Currently, in this pandemic situation, many leaders including federal and state government officials have shown great concern towards society by providing statements such as preventive measures, which were regularly recorded and elaborated in the form of stories by the news channels in their own websites. But, in general, the type of statement given by a leader will always affect people’s opinion and behavior, which also happened during this pandemic. There were variations in number of cases of COVID-19 based upon a region leader’s statements as per timeline and the people’s modulating lifestyle there. This kind of relationship has motivated us towards analyzing the COVID-19 data based on sentiment and emotion involved in leader’s statements, giving rise to an idea of web scraping to obtain their statements and stories from news channels. The main aim of this paper is to enlighten about different techniques and libraries used for web scraping, some challenges while designing a web scraper and finally arrive at an efficient methodology using them to extract news headlines and stories and create a new dataset that can be used in sentiment and emotion analysis. In future work, we will present the results of our experiments with the downloaded datasets.


Web Scraping of COVID-19 News Stories to Create Datasets for Sentiment and Emotion Analysis

The world’s population is ageing rapidly while cognitive impairment in older ages is more evident than ever. Hence, the health and wellbeing among the elderly is an issue of paramount importance. The speedy growth of technology and its application within healthcare has advanced computerized cognitive interventions, which aim to delay the onset and potential progression of cognitive decline. Consequently, the traditional cognitive interventions have made a swift to novel multimedia systems to promote independent, active and healthy ageing. The primary objective of the present study is to introduce an innovative computerized cognitive gamified training and social activation program for health and mildly cognitive impaired older adults. The platform is designed as an extension and adaptation of a previous computerized cognitive training program called ‘SOCIABLE’. Evaluation of the newly designed platform through a single-arm feasibility study followed by a full randomized double-blind controlled trial is recommended as future steps for this piece of work.

Design, method and apparatus of a computerized cognitive gamified training program targeting to maintain and improve cognitive health in older adults

A highly user-centered design approach for developing a mobile health app for pain management (PainApp)

Over the course of the coming decades, the societal goal of maximizing the number of older adults who experience positive trajectories of ageing has been established. To this end, innovative technologybased interventions tailored to older adults’ functional levels and focused on healthy lifestyles are considered imperative. The purpose of this work is to present a cluster of digital solutions aims at supporting and maintaining healthy and independent living for older adults, who face permanently or temporarily reduced functionality and capabilities.

Assistive Technologies for Supporting Wellbeing of Older Adults

Inferring the emotional state of an individual by viewing his/her facial expression seems to be present in all human cultures. Numerous
studies have shown that various changes in facial muscles determine the resulting facial expression. The analysis of images of faces
expressing emotional states promises to contribute to the quantification
of the claimed observations. Here, we use a suite of AI (artificial
intelligence) algorithms, along with ML (maximum likelihood) estimated distributions to quantify the shift in facial expression from
“neutral”→“fear” and “pain”→“pleasure”. The images are single
frames of five emotional states (neutral, fear, pain, pleasure, laugh)
expressed by actors and actresses in BDSM videos. We extract a feature vector for each image, dimension-reduce these feature vectors
by mapping them onto a two-dimensional manifold and calculate
the norms of the normalized displacement vectors for each emotional pair. We then find that the ML distributions of the norms are
Gamma-distributed and that the modes for each pair are different
for both males and females. We use Wilks lambda to determine
significance. We find that the distributions for the females are significantly different, but not for the males. The methodology we present
here has widespread applications: monitoring the emotional states
of humans in various settings; among these: determining whether
participants in BDSM and similar videos are indeed volunteering
their participation or are victims of criminal activity.

Visual Analysis of Emotions Using AI Image-Processing Software: Possible Male/Female Differences between the Emotion Pairs “Neutral”–“Fear” and “Pleasure”–“Pain”

As the field of robotics develops, so do the methods of interfacing with and controlling those robots. Many modern robots can be communicated with using commands that require neither speech nor touch. The two main motivations behind this trend are the assistance of people with disabilities and the desire for more natural Human-Robot Interaction (HRI). In allowing nonverbal communication with robots, accessibility of the systems increases to allow more people to interact with and benefit from robotics systems. Additionally, nonverbal communication provides more natural communication, which can lead to many benefits in HRI. This paper
provides an overview of existing technologies in touchless and nonverbal human-robot interfaces; it presents the most prevalent interface methods, their uses, and limitations. Discussions are provided on future directions for research.

Interfacing with Robots without the use of Touch or Speech

Force control is nowadays a mature technology and it is becoming more and more common in severed robotic applications. Despite this, an effective standardized and comprehensive method to assess force control performance does not exist. Defining the performance of a force-controlled system is not a trivial task, and one of the reason is that performance does not only depend on the dynamics of the robot, but also on the dynamics of the environment, which are usually uncertain and time-varying. Exerting a force on a soft environment is different from exerting a force on a rigid environment. Indeed, the same force-controlled robot can have different force responses in different environments. This paper proposes a methodology to standardize the benchmarking of force control algorithms applied to different actuation architectures and considering a wide set of possible environments and disturbances, defined by the application of interest.

Benchmarking Force Control Algorithms

We are presenting a conceptual design of a post-stroke rehabilitation
device, which is aimed for training the coupled movement
of wrist and fingers from flexion to extension in three modalities –
passive, active-assisted and active mode of operation. We will describe the configuration, actuation and control system of the
robotic device and provide the results of the mechanism’s mock-up
evaluation, therapists’ feedback on the design, future work and
development plans.

Building Extraction from RGB Satellite Images using Deep Learning: A U-Net Approach

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Map Visualization using Spatial and Spatio-Temporal Data: Application to COVID-19 Data

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Building Extraction from RGB Satellite Images using Deep Learning: A U-Net Approach

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Next from PETRA 2021

Map Visualization using Spatial and Spatio-Temporal Data: Application to COVID-19 Data

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Human Segmentation with Dynamic LiDAR Data

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